EcoService Models Library (ESML)
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Which comparison is best for me?EM Variables by Variable Role
One quick way to compare ecological models (EMs) is by comparing their variables. Predictor variables show what kinds of influences a model is able to account for, and what kinds of data it requires. Response variables show what information a model is capable of estimating.
This first comparison shows the names (and units) of each EM’s variables, side-by-side, sorted by variable role. Variable roles in ESML are as follows:
- Predictor Variables
- Time- or Space-Varying Variables
- Constants and Parameters
- Intermediate (Computed) Variables
- Response Variables
- Computed Response Variables
- Measured Response Variables
EM Variables by Category
A second way to use variables to compare EMs is by focusing on the kind of information each variable represents. The top-level categories in the ESML Variable Classification Hierarchy are as follows:
- Policy Regarding Use or Management of Ecosystem Resources
- Land Surface (or Water Body Bed) Cover, Use or Substrate
- Human Demographic Data
- Human-Produced Stressor or Enhancer of Ecosystem Goods and Services Production
- Ecosystem Attributes and Potential Supply of Ecosystem Goods and Services
- Non-monetary Indicators of Human Demand, Use or Benefit of Ecosystem Goods and Services
- Monetary Values
Besides understanding model similarities, sorting the variables for each EM by these 7 categories makes it easier to see if the compared models can be linked using similar variables. For example, if one model estimates an ecosystem attribute (in Category 5), such as water clarity, as a response variable, and a second model uses a similar attribute (also in Category 5) as a predictor of recreational use, the two models can potentially be used in tandem. This comparison makes it easier to spot potential model linkages.
All EM Descriptors
This selection allows a more detailed comparison of EMs by model characteristics other than their variables. The 50-or-so EM descriptors for each model are presented, side-by-side, in the following categories:
- EM Identity and Description
- EM Modeling Approach
- EM Locations, Environments, Ecology
- EM Ecosystem Goods and Services (EGS) potentially modeled, by classification system
EM Descriptors by Modeling Concepts
This feature guides the user through the use of the following seven concepts for comparing and selecting EMs:
- Conceptual Model
- Modeling Objective
- Modeling Context
- Potential for Model Linkage
- Feasibility of Model Use
- Model Certainty
- Model Structural Information
Though presented separately, these concepts are interdependent, and information presented under one concept may have relevance to other concepts as well.
Compare Criteria:
Show Criteria
Hide CriteriaEM Identity and Description
EM Identification
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EM ID
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EM-98 
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EM-320 | EM-435 | EM-448 | EM-466 | EM-549 |
EM-796
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EM Short Name
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PATCH, western USA | Coastal protection, Europe | InVESTv3.0 Sed. retention, Guánica Bay, PR, USA | Wave energy attenuation, St. Croix, USVI | Yasso 15 - soil carbon model | Nutrient Tracking Tool (NTT) | Wildflower mix supporting bees, MI, USA |
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EM Full Name
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PATCH (Program to Assist in Tracking Critical Habitat), western USA | Coastal protection, Europe | InVEST (Integrated Valuation of Environmental Services and Tradeoffs)v3.0 Sediment Retention, Guánica Bay, Puerto Rico, USA | Wave energy attenuation (by reef), St. Croix, USVI | Yasso 15 - soil carbon | Nutrient Tracking Tool (NTT) | Wildflower planting mix supporting bees in agricultural landscapes, MI, USA |
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EM Source or Collection
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US EPA | EU Biodiversity Action 5 | US EPA | InVEST | US EPA | None | None | None |
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EM Source Document ID
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2 | 296 | 338 | 335 |
342 ?Comment:Webpage pdf users manual for model. |
352 | 400 |
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Document Author
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Carroll, C, Phillips, M. K. , Lopez-Gonzales, C. A and Schumaker, N. H. | Liquete, C., Zulian, G., Delgado, I., Stips, A., and Maes, J. | Amelia Smith, Susan Harrell Yee, Marc Russell, Jill Awkerman and William S. Fisher | Yee, S. H., Dittmar, J. A., and L. M. Oliver | Repo, A., Jarvenpaa, M., Kollin, J., Rasinmaki, J. and Liski, J. | Saleh, A. and O. Gallego | Williams, N.M., Ward, K.L., Pope, N., Isaacs, R., Wilson, J., May, E.A., Ellis, J., Daniels, J., Pence, A., Ullmann, K., and J. Peters |
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Document Year
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2006 | 2013 | 2017 | 2014 | 2016 | 2018 | 2015 |
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Document Title
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Defining recovery goals and strategies for endangered species: The wolf as a case study | Assessment of coastal protection as an ecosystem service in Europe | Linking ecosystem services supply to stakeholder concerns on both land and sea: An example from Guanica Bay watershed, Puerto Rico | Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI | Yasso 15 graphical user-interface manual | Nutrient Tracking Tool (NTT) User Manual | Native wildflower Plantings support wild bee abundance and diversity in agricultural landscapes across the United States |
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Document Status
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Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Other or unclear (explain in Comment) | Peer reviewed and published | Peer reviewed and published |
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Comments on Status
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Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Not applicable | webpage | Published journal manuscript |
Software and Access
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EM ID
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EM-98
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EM-320 | EM-435 | EM-448 | EM-466 | EM-549 |
EM-796
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| Not applicable | Not applicable | http://www.naturalcapitalproject.org/invest/ | Not applicable |
http://en.ilmatieteenlaitos.fi/yasso-download-and-support ?Comment:User's manual states that the software will be downloadable at this site. |
http://ntt.tiaer.tarleton.edu/welcomes/new?locale=en | Not applicable | |
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Contact Name
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Carlos Carroll | Camino Liquete | Susan H. Yee | Susan H. Yee | Jari Liski |
Ali Saleh ?Comment:Phone # 254-968-9079 |
Neal Williams |
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Contact Address
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Klamath Center for Conservation Research, Orleans, CA 95556 | European Commission, Joint Research Centre, Institute for Environment and Sustainability, Via E. Fermi 2749, I-21027 Ispra, VA, Italy | U.S. Environmental Protection Agency, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | Finnish Meteorological Institute, P.O. Box 503, 00101 Helsinki | Associate Director, Texas Institute for Applied Environmental Research, P.O. Box T410, Tarleton State University Stephenville, TX 76402 | Department of Entomology and Mematology, Univ. of CA, One Shilds Ave., Davis, CA 95616 |
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Contact Email
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carlos@cklamathconservation.org | camino.liquete@gmail.com | yee.susan@epa.gov | yee.susan@epa.gov | jari.liski@ymparisto.fi | saleh@tarleton.edu | nmwilliams@ucdavis.edu |
EM Description
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EM ID
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EM-98
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EM-320 | EM-435 | EM-448 | EM-466 | EM-549 |
EM-796
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Summary Description
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**Note: A more recent version of this model exists. See Related EMs below for links to related models/applications.** AUTHORS' DESCRIPTION: "PATCH (program to assist in tracking critical habitat), the SEPM used here, is designed for studying territorial vertebrates. It links the survival and fecundity of individual animals to geographic information system (GIS) data on mortality risk and habitat productivity at the scale of an individual or pack territory. Territories are allocated by intersecting the GIS data with an array of hexagonal cells. The different habitat types in the GIS maps are assigned weights based on the relative levels of fecundity and survival expected in those habitat classes. Base survival and reproductive rates, derived from published field studies, are then supplied to the model as a population projection matrix. The model scales these base matrix values using the mean of the habitat weights within each hexagon, with lower means translating into lower survival rates or reproductive output. Each individual in the population is tracked through a yearly cycle of survival, fecundity, and dispersal events. Environmental stochasticity is incorporated by drawing each year’s base population matrix from a randomized set of matrices whose elements were drawn from a beta (survival) or normal (fecundity) distribution. Adult organisms are classified as either territorial or floaters. The movement of territorial individuals is governed by a parameter for site fidelity, but floaters must always search for available breeding sites. As pack size increases, pack members in the model have a greater tendency to disperse and search for new available breeding sites. Movement decisions use a directed random walk that combines varying proportions of randomness, correlation, and attraction to higher-quality habitat (Schumaker 1998)." | ABSTRACT: "Mapping and assessment of ecosystem services is essential to provide scientific support to global and EU biodiversity policy. Coastal protection has been mostly analysed in the frame of coastal vulnerability studies or in local, habitat-specific assessments. This paper provides a conceptual and methodological approach to assess coastal protection as an ecosystem service at different spatial–temporal scales, and applies it to the entire EU coastal zone. The assessment of coastal protection incorporates 14 biophysical and socio-economic variables from both terrestrial and marine datasets. Those variables define three indicators: coastal protection capacity, coastal exposure and human demand for protection. A questionnaire filled by coastal researchers helped assign ranks to categorical parameters and weights to the individual variables. The three indicators are then framed into the ecosystem services cascade model to estimate how coastal ecosystems provide protection, in particular describing the service function, flow and benefit. The results are comparative and aim to support integrated land and marine spatial planning. The main drivers of change for the provision of coastal protection come from the widespread anthropogenic pressures in the European coastal zone, for which a short quantitative analysis is provided." | Please note: This ESML entry describes a specific, published application of an InVEST model. Different versions (e.g. different tiers) or more recent versions of this model may be available at the InVEST website. AUTHOR'S DESCRIPTION: "…were identified as relevant to stakeholder objectives in the Guanica Bay watershed identified during the 2013 Public Values Forum…Ecological production fuctions were applied to translate LULC measures of ecosystem conditions to supply of ecosystem services…Sediment retention in each watershed depends on geomorphology, climate, vegetation, and management, and was estimated by applying the Universal Soil Loss Equation (USLE) in each HUCH12 sub-watershed using a sediment retention model (InVEST 3.0.0…" | ABSTRACT: "...We investigated and compared a number of existing methods for quantifying ecological integrity, shoreline protection, recreational opportunities, fisheries production, and the potential for natural products discovery from reefs. Methods were applied to mapping potential ecosystem services production around St. Croix, U.S. Virgin Islands. Overall, we found that a number of different methods produced similar predictions." AUTHOR'S DESCRIPTION: "A number of methods have been developed for linking biophysical attributes of reef condition, such as reef structural complexity, fish biomass, or species richness, to provisioning of ecosystem goods and services (Principe et al., 2012). We investigated the feasibility of using existing methods and data for mapping production of reef ecosystem goods and services. We applied these methods toward mapping potential ecosystem goods and services production in St. Croix, U.S. Virgin Islands (USVI)...For each of the five categories of ecosystem services, we chose a suite of models and indices for estimating potential production based on relative ease of implementation, consisting of well-defined parameters, and likely availability of input data, to maximize potential for transferability to other locations. For each method, we assembled the necessary reef condition and environmental data as spatial data layers for St. Croix (Table1). The coastal zone surrounding St. Croix was divided into 10x10 m grid cells, and production functions were applied to quantify ecosystem services provisioning in each grid cell...Shoreline protection as an ecosystem service has been defined in a number of ways including protection from shoreline erosion, storm damage, or coastal inundation during extreme events (UNEP-WCMC (United Nations Environment Programme, World Conservation Monitoring Centre), 2006; WRI (World Resources Institute), 2009), but is often quantified as wave energy attenuation, an intermediate service that contributes to shoreline protection by reducing rates of erosion or coastal inundation (Principeet al., 2012)...The energy (attenuation) in a moving wave (E) can then be calculated by E = 1/8ρgH^2 where ρ is the density of seawater (1025 kg m^-3) and H is wave height (attenuation)." | AUTHOR'S DESCRIPTION: "The Yasso15 calculates the stock of soil organic carbon, changes in the stock of soil organic carbon and heterotrophic soil respiration. Applications the model include, for example, simulations of land use change, ecosystem management, climate change, greenhouse gas inventories and education. The Yasso15 is a relatively simple soil organic carbon model requiring information only on climate and soil carbon input to operate... In the Yasso15 model litter is divided into five soil organic carbon compound groups (Fig. 1). These groups are compounds hydrolysable in acid (denoted with A), compounds soluble in water (W) or in a non-polar solvent, e.g. ethanol or dichloromethane (E), compounds neither soluble nor hydrolysable (N) and humus (H). The AWEN form the group of labile fractions whereas H fraction contains humus, which is more recalcitrant to decomposition. Decomposition of the fractions results in carbon flux out of soil and carbon fluxes between the compartments (Fig. 1). The basic idea of Yasso15 is that the decomposition of different types of soil carbon input depends on the chemical composition of the input types and climate conditions. The effects of the chemical composition are taken into account by dividing carbon input to soil between the four labile compartments explicitly according to the chemical composition (Fig. 1). Decomposition of woody litter depends additionally on the size of the litter. The effects of climate conditions are modelled by adjusting the decomposition rates of the compartments according to air temperature and precipitation. In the Yasso15 model separate decomposition rates are applied to fast-decomposing A, W and E compartments, more slowly decomposing N and very slowly decomposing humus compartment H. The Yasso is a global-level model meaning that the same parameter values are suitable for all applications for accurate predictions. However, the current GUI version also includes possibility to use earlier parameterizations. The parameter values of Yasso15 are based on measurements related to cycling of organic carbon in soil (Table 1). An extensive set of litter decomposition measurements was fundamental in developing the model (Fig. 2). This data set covered, firstly, most of the global climate conditions in terms of temperature precipitation and seasonality (Fig 3.), secondly, different ecosystem types from forests to grasslands and agricultural fields and, thirdly, a wide range of litter types. In addition, a large set of data giving information on decomposition of woody litter (including branches, stems, trunks, roots with different size classes) was used for fitting. In addition to woody and non-woody litter decomposition measurements, a data set on accumulation of soil carbon on the Finnish coast and a large, global steady state data sets were used in the parameterization of the model. These two data sets contain information on the formation and slow decomposition of humus." | AUTHOR'S DESCRIPTION: "The Nutrient Tracking Tool (NTT) was designed and developed by the Texas Institute for Applied Environmental Research (TIAER), Tarleton State University with funding from USDA Office of Environmental Markets, USDA-NRCS Conservation Innovation Grants program, and various state agencies. NTT is a web-based, site-specific application that estimates nutrient and sediment losses at the field scale or at the small watershed scale. Agricultural producers and land managers can define a number of management scenarios and generate a report showing the expected nutrient loss differences between any selected scenarios for a given field or small watershed. NTT compares agricultural management systems to calculate a change in expected flow, nitrogen, phosphorus, sediment losses, and crop yield. Estimates are made using the APEX model (Williams et al. 2000). Results represent average losses from the field based on 35 years of simulated weather. NTT requires regional soils, climate and site-specific crop management information. NTT currently provides selections for all regions of U.S. and Puerto Rico territory, but it has only been validated for a limited number of states and counties. As validation becomes possible in other parts of the country, parameter files may be updated for additional counties in future versions. There are two versions of new NTT program available: The BASIC version is a user-friendly version of NTT that allows users to estimate N, P and sediment from crop and pasture lands. The Research and Education version of NTT (NTT-RE) was developed for researchers and educational institutes for teaching and training purposes. NTT-RE includes additional functions allowing the user to view and edit soil layers, view crop water and nutrient stresses, and modify and the APEX parameters file for calibration and validation purposes. The data sources and APEX simulations in both versions are identical. For more information regarding NTT, please refer to Saleh et al. (2011 and 2015)." | Abstract: " Global trends in pollinator-dependent crops have raised awareness of the need to support managed and wild bee populations to ensure sustainable crop production. Provision of sufficient forage resources is a key element for promoting bee populations within human impacted landscapes, particularly those in agricultural lands where demand for pollination service is high and land use and management practices have reduced available flowering resources. Recent government incentives in North America and Europe support the planting of wildflowers to benefit pollinators; surprisingly, in North America there has been almost no rigorous testing of the performance of wildflower mixes, or their ability to support wild bee abundance and diversity. We tested different wildflower mixes in a spatially replicated, multiyear study in three regions of North America where production of pollinatordependent crops is high: Florida, Michigan, and California. In each region, we quantified flowering among wildflower mixes composed of annual and perennial species, and with high and low relative diversity. We measured the abundance and species richness of wild bees, honey bees, and syrphid flies at each mix over two seasons. In each region, some but not all wildflower mixes provided significantly greater floral display area than unmanaged weedy control plots. Mixes also attracted greater abundance and richness of wild bees, although the identity of best mixes varied among regions. By partitioning floral display size from mix identity we show the importance of display size for attracting abundant and diverse wild bees. Season-long monitoring also revealed that designing mixes to provide continuous bloom throughout the growing season is critical to supporting the greatest pollinator species richness. Contrary to expectation, perennials bloomed in their first season, and complementarity in attraction of pollinators among annuals and perennials suggests that inclusion of functionally diverse species may provide the greatest benefit. Wildflower mixes may be particularly important for providing resources for some taxa, such as bumble bees, which are known to be in decline in several regions of North America. No mix consistently attained the full diversity that was planted. Further study is needed on how to achieve the desired floral display and diversity from seed mixes. " Additional information in supplemental Appendices online: http://dx.doi.org/10.1890/14-1748.1.sm |
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Specific Policy or Decision Context Cited
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AUTHOR DESCRIPTION: "Comprehensive habitat and viability assessments. . . [more rigoursly defined] can clarify debate of goals for recovery of large carnivores"; Endangered Species Act and related litigation | Supports global and EU biodiversity policy | None identified | None identified | None identified | None identified | None identrified |
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Biophysical Context
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Great Plains to Pacific Coast, northern Rocky Mountains, Pacific Northwest | No additional description provided | No additional description provided | No additional description provided | Not applicable | No additional description provided | field plots near agricultural fruit and vegetable research farms |
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EM Scenario Drivers
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Population growth, road development (density) on public vs private land | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | Varied wildflower planting mixes of annuals and perennials |
EM Relationship to Other EMs or Applications
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EM ID
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EM-98
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EM-320 | EM-435 | EM-448 | EM-466 | EM-549 |
EM-796
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Method Only, Application of Method or Model Run
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Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application | Method + Application | Method Only | Method Only | Method + Application (multiple runs exist) View EM Runs |
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New or Pre-existing EM?
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New or revised model | New or revised model | Application of existing model | Application of existing model | New or revised model | New or revised model | New or revised model |
Related EMs (for example, other versions or derivations of this EM) described in ESML
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EM ID
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EM-98
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EM-320 | EM-435 | EM-448 | EM-466 | EM-549 |
EM-796
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Document ID for related EM
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Doc-328 | Doc-337 | None | Doc-309 | Doc-335 | Doc-343 | Doc-344 | None | Doc-400 |
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EM ID for related EM
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EM-403 | EM-422 | None | EM-359 | EM-447 | EM-449 | EM-467 | EM-469 | EM-480 | EM-485 | EM-584 | EM-784 |
EM Modeling Approach
EM Relationship to Time
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EM ID
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EM-98
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EM-320 | EM-435 | EM-448 | EM-466 | EM-549 |
EM-796
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EM Temporal Extent
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2000-2025 | 1992-2010 | 1978 - 2013 | 2006-2007, 2010 | Not applicable | 35 yr | 2010-2011 |
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EM Time Dependence
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time-dependent | time-stationary | time-stationary | time-stationary | time-dependent | time-dependent | time-dependent |
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EM Time Reference (Future/Past)
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future time | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | past time |
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EM Time Continuity
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discrete | Not applicable | Not applicable | Not applicable | discrete | discrete | discrete |
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EM Temporal Grain Size Value
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1 | Not applicable | Not applicable | Not applicable | 1 | 1 | 1 |
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EM Temporal Grain Size Unit
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Year | Not applicable | Not applicable | Not applicable | Year | Day | Year |
EM Spatial Extent
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EM ID
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EM-98
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EM-320 | EM-435 | EM-448 | EM-466 | EM-549 |
EM-796
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Bounding Type
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Physiographic or ecological | Geopolitical | Watershed/Catchment/HUC | Physiographic or ecological | Not applicable | Not applicable |
Point or points ?Comment:This is a guess based on information in the document. 3 field sites were separated by up to 9km |
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Spatial Extent Name
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Western United States | Shoreline of the European Union-27 | Guanica Bay watershed | Coastal zone surrounding St. Croix | Not applicable | Not applicable | Agricultural plots |
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Spatial Extent Area (Magnitude)
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>1,000,000 km^2 | >1,000,000 km^2 | 1000-10,000 km^2. | 100-1000 km^2 | Not applicable | Not applicable | 10-100 km^2 |
Spatial Distribution of Computations
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EM ID
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EM-98
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EM-320 | EM-435 | EM-448 | EM-466 | EM-549 |
EM-796
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EM Spatial Distribution
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spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially lumped (in all cases) | spatially lumped (in all cases) | spatially lumped (in all cases) |
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Spatial Grain Type
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area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | Not applicable | Not applicable | Not applicable |
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Spatial Grain Size
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504 km^2 | Irregular | 30 m x 30 m | 10 m x 10 m | Not applicable | Not applicable | Not applicable |
EM Structure and Computation Approach
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EM ID
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EM-98
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EM-320 | EM-435 | EM-448 | EM-466 | EM-549 |
EM-796
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EM Computational Approach
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Numeric | Analytic | Analytic | Analytic | Numeric | Numeric | Numeric |
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EM Determinism
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stochastic | deterministic | deterministic | deterministic | stochastic | deterministic | deterministic |
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Statistical Estimation of EM
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Model Checking Procedures Used
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EM ID
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EM-98
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EM-320 | EM-435 | EM-448 | EM-466 | EM-549 |
EM-796
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Model Calibration Reported?
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Unclear | No | No | Yes | Not applicable | Not applicable | No |
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Model Goodness of Fit Reported?
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No | No | No | No | Not applicable | Not applicable | No |
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Goodness of Fit (metric| value | unit)
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None | None | None | None | None | None | None |
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Model Operational Validation Reported?
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No | No | No | Yes | Not applicable | Unclear | No |
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Model Uncertainty Analysis Reported?
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No | No | No | No | Not applicable | Not applicable | No |
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Model Sensitivity Analysis Reported?
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Yes ?Comment:No results reported. Just a general statement was made about PATCH sensitivity and that demographic parameters are more sensitive that variation in other parameters such as dispersadistance . Reference made to another publication Carroll et al. 2003. Use of population viability analysis and reserve slelection algorithms in regional conservation plans. Ecol. App. 13:1773-1789. |
No | No | No | Not applicable | Not applicable | No |
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Model Sensitivity Analysis Include Interactions?
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Unclear | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
EM Locations, Environments, Ecology
Location of EM Application
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
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EM-98
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EM-320 | EM-435 | EM-448 | EM-466 | EM-549 |
EM-796
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None | None | None |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
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EM-98
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EM-320 | EM-435 | EM-448 | EM-466 | EM-549 |
EM-796
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None |
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None | None | None |
Centroid Lat/Long (Decimal Degree)
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EM ID
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EM-98
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EM-320 | EM-435 | EM-448 | EM-466 | EM-549 |
EM-796
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Centroid Latitude
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39.88 | 48.2 | 17.96 | 17.73 | Not applicable | Not applicable | 43.87 |
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Centroid Longitude
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-113.81 | 16.35 | -67.02 | -64.77 | Not applicable | Not applicable | -85.64 |
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Centroid Datum
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WGS84 | WGS84 | WGS84 | WGS84 | Not applicable | Not applicable | WGS84 |
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Centroid Coordinates Status
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Estimated | Estimated | Estimated | Estimated | Not applicable | Not applicable | Provided |
Environments and Scales Modeled
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EM ID
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EM-98
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EM-320 | EM-435 | EM-448 | EM-466 | EM-549 |
EM-796
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EM Environmental Sub-Class
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Terrestrial Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified) | Near Coastal Marine and Estuarine | Forests | Grasslands | Scrubland/Shrubland | Tundra | Agroecosystems | Agroecosystems |
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Specific Environment Type
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Not reported | Coastal zones | None reported | Coral reefs | Not applicable | Agroecosystems | Agricultural landscape |
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EM Ecological Scale
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Ecological scale corresponds to the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class |
Scale and taxa of organisms modeled
Scale of differentiation of organisms modeled
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EM ID
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EM-98
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EM-320 | EM-435 | EM-448 | EM-466 | EM-549 |
EM-796
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EM Organismal Scale
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Species | Not applicable | Not applicable | Not applicable | Species | Not applicable | Species |
Taxonomic level and name of organisms or groups identified
taxonomyHelp
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EM-98
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EM-320 | EM-435 | EM-448 | EM-466 | EM-549 |
EM-796
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None Available | None Available | None Available | None Available | None Available |
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EnviroAtlas URL
EM Ecosystem Goods and Services (EGS) potentially modeled, by classification system
CICES v 4.3 - Common International Classification of Ecosystem Services (Section > Division > Group > Class)
em.detail.cicesHelp
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EM-98
|
EM-320 | EM-435 | EM-448 | EM-466 | EM-549 |
EM-796
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None |
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<a target="_blank" rel="noopener noreferrer" href="https://www.epa.gov/eco-research/national-ecosystem-services-classification-system-nescs-plus">National Ecosystem Services Classification System (NESCS) Plus</a>
fegs1Help
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(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)
fegs2Help
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EM-98
|
EM-320 | EM-435 | EM-448 | EM-466 | EM-549 |
EM-796
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None | None | None |
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